Hidden Markov model , domain

Heparin Sulfate Proteoglycans Hepatic Lipase Hepatitis Hepatitis C Heptahelical Domain Heptahelical Receptors HERG-channels Heterologous Desensitization Heterologous Expression System Heterotrimeric G-Proteins Hidden Markov Model High-density Lipoprotein (HDL)... 

The parsing of the transporter sequences into the TM domains shown in Fig. 1A represents the consensus result of three different methods. Average hydrophobicity was calculated with ProperTM using different window sizes and the Kyte and Doolittle scale (7). TMHMM, a hidden Markov model-based approach (8), and PHDHTM, a profile-based neural network method (9), were then utilized to refine the predictions. 

Pattern and motif analysis Motif Scan PROSITE Pfam http //myhits.isb-sib.ch/cgi-bin/motif scan http //us.expasy.org/prosite/ http //www.sanger.ac.uk/Software/Pfam/ Einding motifs in a sequence. Protein fanulies and domains. Protein fanulies database of hidden Markov models (HMMs). 

Figure 1. Schematic domain structures are shown for homologous villidin proteins from D. d/sco/deum (XP 636652,1704aa) and E. histolytica ("ABPH", AF118397,1602aa). Both contain typical coronin domains at their amino termini in addition to 3 pleckstrin homology (PH), 3-5 gelsolin (C) and a villin headpiece (VHP) domains at the C-termini, all detected by matches to the corresponding hidden Markov models (HMM) from the PFAM database (http //www. sanger.ac.uk/Software/Pfam/).

G Fan and XG Xia. Improved hidden Markov models in the wavelet-domain. IEEE Trans, on Signal Processing, 49 115-120, 2001. 

JK Romberg, H Choi, and RG Baraniuk. Bayesian tree structured image modeling using wavelet-domain hidden Markov models. IEEE Trans, on Image Processing, 10 1056-1068, 2001. 

W Sun, A Palazoglu, and JA Romagnoli. Detecting abnormal process trends by wavelet-domain hidden Markov models. AIChE J., 749 140-150, 2003. 

As has been described in Sect. 5.3, the conservation patterns of enzymes are often indicative of the particular family they belong to and can be used for their classification. However, the iterative searches and multiple alignment methods used for their establishment require a certain bioinformatic infrastructure as well as some experience with these issues. If the goal of the analysis is not the detection of novel enzyme families, but rather the classification of a novel sequence into one of the already existing enzyme families, there are a number of protein domain and motif databases that will be useful in this respect[60 61. These databases do not store the sequences themselves but rather work with descriptors of protein families and protein domains. These descriptors can consist of the Profiles or Hidden Markov Models mentioned above, but other types are also being used. With a particular... 

PFAM is a database of Hidden Markov Models of protein families and domains, maintained at the Sanger Centre in Cambridge1651. The concept of PFAM is comparable to that of the PROSITE profile section. Similar to the profiles, the HMMs in PFAM have been derived by the iterative refinement procedure mentioned in Sect. 5.2.4. Unlike the PROSITE profiles, which all have been created manually by the curators, the HMMs in PFAM are generated semi-automatically, which accounts for a slightly lower sensitivity. However, this lack is more than compensated for by the facilitated update procedure, allowing the database to grow much faster than PROSITE and to have a shorter generation cycle. Currently, PFAM holds 2727 entries. 

Presumably, intron loss occurred in the time interval elapsing between the eumetazoan ancestor and the appearance of the common ancestor of choanoflagellates and metazoans in the Precambrian sea over 600 years ago. Protein domains PFAM and SMART (containing 685 hidden Markov models, http/Zsmart.embl-heidelberg)... 

As a stochastic process. Hidden Markov Model (HMM) has been successfully applied in many domains, such as Speech recognition, genes and Deoxyribonucleic acid (DNA) analysis (Rabiner 1989) thanks to its strong mathematical basis. In the CBM context, HMMs can divide equipment conditions into several meaningful states, such as good , minor defect , maintenance required and failure and therefore easy to understand... 

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